MSAProbs-MPI
MSAProbs-MPI performs parallel multiple sequence alignment of protein sequences on distributed-memory multicore clusters using Message Passing Interface (MPI) to provide accurate and scalable alignments.
Key Features:
- Parallelization with MPI: Distributes computational tasks across multiple processors in a cluster using Message Passing Interface (MPI) for distributed-memory parallelism.
- High accuracy: Retains MSAProbs' probabilistic/statistical models to capture biological nuances in protein sequence data and produce reliable alignments.
- Scalability and efficiency: Optimized for high-performance computing environments to handle large datasets with reduced runtime on multicore clusters.
- Based on MSAProbs v0.9.7: Implements the algorithms and statistical frameworks of MSAProbs version 0.9.7 in a parallelized architecture.
- Configurable performance parameters: Exposes parameters to adjust computation and resource usage according to hardware configurations and dataset characteristics.
Scientific Applications:
- Phylogenetic analysis: Produces multiple sequence alignments used to infer evolutionary relationships and build phylogenetic trees.
- Protein structure prediction: Provides accurate alignments of homologous protein sequences that inform comparative modelling and structure prediction.
- Functional annotation of genes: Generates alignments that support transfer of functional annotations between conserved sequences.
- Evolutionary studies: Enables analysis of sequence conservation, divergence, and evolutionary patterns across large protein datasets.
Methodology:
Applies MSAProbs' probabilistic/statistical alignment models and distributes computations across cluster nodes using Message Passing Interface (MPI), with configurable parameters for performance tuning.
Topics
Details
- Tool Type:
- command-line tool
- Added:
- 1/18/2021
- Last Updated:
- 3/1/2021
Operations
Publications
González-Domínguez J. Fast and Accurate Multiple Sequence Alignment with MSAProbs-MPI. Methods in Molecular Biology. 2020. doi:10.1007/978-1-0716-1036-7_3. PMID:33289885.
PMID: 33289885